{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:42:56.890367Z",
     "start_time": "2025-05-18T08:42:56.857791Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# 加载数据\n",
    "\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "train['is_train'] = 1\n",
    "test['is_train'] = 0\n",
    "all_data = pd.concat([train, test], axis=0).reset_index(drop=True)\n",
    "all_data"
   ],
   "id": "15f1152aa2b745a2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "2914  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "2915  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "2916  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "2917  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "2918  2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolQC  Fence MiscFeature MiscVal MoSold  \\\n",
       "0            Lvl    AllPub  ...    NaN    NaN         NaN       0      2   \n",
       "1            Lvl    AllPub  ...    NaN    NaN         NaN       0      5   \n",
       "2            Lvl    AllPub  ...    NaN    NaN         NaN       0      9   \n",
       "3            Lvl    AllPub  ...    NaN    NaN         NaN       0      2   \n",
       "4            Lvl    AllPub  ...    NaN    NaN         NaN       0     12   \n",
       "...          ...       ...  ...    ...    ...         ...     ...    ...   \n",
       "2914         Lvl    AllPub  ...    NaN    NaN         NaN       0      6   \n",
       "2915         Lvl    AllPub  ...    NaN    NaN         NaN       0      4   \n",
       "2916         Lvl    AllPub  ...    NaN    NaN         NaN       0      9   \n",
       "2917         Lvl    AllPub  ...    NaN  MnPrv        Shed     700      7   \n",
       "2918         Lvl    AllPub  ...    NaN    NaN         NaN       0     11   \n",
       "\n",
       "     YrSold SaleType  SaleCondition  SalePrice  is_train  \n",
       "0      2008       WD         Normal   208500.0         1  \n",
       "1      2007       WD         Normal   181500.0         1  \n",
       "2      2008       WD         Normal   223500.0         1  \n",
       "3      2006       WD        Abnorml   140000.0         1  \n",
       "4      2008       WD         Normal   250000.0         1  \n",
       "...     ...      ...            ...        ...       ...  \n",
       "2914   2006       WD         Normal        NaN         0  \n",
       "2915   2006       WD        Abnorml        NaN         0  \n",
       "2916   2006       WD        Abnorml        NaN         0  \n",
       "2917   2006       WD         Normal        NaN         0  \n",
       "2918   2006       WD         Normal        NaN         0  \n",
       "\n",
       "[2919 rows x 82 columns]"
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
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       "      <th>SalePrice</th>\n",
       "      <th>is_train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>2915</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2916</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>2917</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2917</th>\n",
       "      <td>2918</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2919</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "<p>2919 rows × 82 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 97
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:42:58.216318Z",
     "start_time": "2025-05-18T08:42:58.182008Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 缺失值处理\n",
    "# 数值型特征用中位数填充，类别型特征用众数填充\n",
    "num_cols = all_data.select_dtypes(include=np.number).columns\n",
    "cat_cols = all_data.select_dtypes(include='object').columns\n",
    "\n",
    "num_imputer = SimpleImputer(strategy='median')\n",
    "all_data[num_cols] = num_imputer.fit_transform(all_data[num_cols])\n",
    "\n",
    "cat_imputer = SimpleImputer(strategy='most_frequent')\n",
    "all_data[cat_cols] = cat_imputer.fit_transform(all_data[cat_cols])\n",
    "\n",
    "all_data"
   ],
   "id": "49ceb735ff02dc89",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1.0        60.0       RL         65.0   8450.0   Pave  Grvl      Reg   \n",
       "1        2.0        20.0       RL         80.0   9600.0   Pave  Grvl      Reg   \n",
       "2        3.0        60.0       RL         68.0  11250.0   Pave  Grvl      IR1   \n",
       "3        4.0        70.0       RL         60.0   9550.0   Pave  Grvl      IR1   \n",
       "4        5.0        60.0       RL         84.0  14260.0   Pave  Grvl      IR1   \n",
       "...      ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "2914  2915.0       160.0       RM         21.0   1936.0   Pave  Grvl      Reg   \n",
       "2915  2916.0       160.0       RM         21.0   1894.0   Pave  Grvl      Reg   \n",
       "2916  2917.0        20.0       RL        160.0  20000.0   Pave  Grvl      Reg   \n",
       "2917  2918.0        85.0       RL         62.0  10441.0   Pave  Grvl      Reg   \n",
       "2918  2919.0        60.0       RL         74.0   9627.0   Pave  Grvl      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolQC  Fence MiscFeature MiscVal MoSold  \\\n",
       "0            Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0    2.0   \n",
       "1            Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0    5.0   \n",
       "2            Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0    9.0   \n",
       "3            Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0    2.0   \n",
       "4            Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0   12.0   \n",
       "...          ...       ...  ...    ...    ...         ...     ...    ...   \n",
       "2914         Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0    6.0   \n",
       "2915         Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0    4.0   \n",
       "2916         Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0    9.0   \n",
       "2917         Lvl    AllPub  ...     Ex  MnPrv        Shed   700.0    7.0   \n",
       "2918         Lvl    AllPub  ...     Ex  MnPrv        Shed     0.0   11.0   \n",
       "\n",
       "      YrSold SaleType  SaleCondition  SalePrice  is_train  \n",
       "0     2008.0       WD         Normal   208500.0       1.0  \n",
       "1     2007.0       WD         Normal   181500.0       1.0  \n",
       "2     2008.0       WD         Normal   223500.0       1.0  \n",
       "3     2006.0       WD        Abnorml   140000.0       1.0  \n",
       "4     2008.0       WD         Normal   250000.0       1.0  \n",
       "...      ...      ...            ...        ...       ...  \n",
       "2914  2006.0       WD         Normal   163000.0       0.0  \n",
       "2915  2006.0       WD        Abnorml   163000.0       0.0  \n",
       "2916  2006.0       WD        Abnorml   163000.0       0.0  \n",
       "2917  2006.0       WD         Normal   163000.0       0.0  \n",
       "2918  2006.0       WD         Normal   163000.0       0.0  \n",
       "\n",
       "[2919 rows x 82 columns]"
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>RL</td>\n",
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       "      <td>8450.0</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>MnPrv</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>2915.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2916.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 82 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 98
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:43:04.876531Z",
     "start_time": "2025-05-18T08:43:04.823512Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 设置支持中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 正确显示负号\n",
    "# 通过箱线图检测异常值（以GrLivArea为例）\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.boxplot(x=all_data['GrLivArea'])\n",
    "plt.title('GrLivArea异常值检测')\n",
    "plt.show()\n",
    "\n",
    "# 剔除GrLivArea > 4000的极端值\n",
    "all_data = all_data[all_data['GrLivArea'] <= 4000]"
   ],
   "id": "3c085413d1b25cbd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 101
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:43:07.755159Z",
     "start_time": "2025-05-18T08:43:07.742701Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 特征工程：创建房龄特征\n",
    "all_data['HouseAge'] = all_data['YrSold'] - all_data['YearBuilt']\n",
    "\n",
    "# 重新更新数值型特征列（包含 HouseAge）\n",
    "num_cols = all_data.select_dtypes(include=np.number).columns"
   ],
   "id": "97ca785be647c0c1",
   "outputs": [],
   "execution_count": 102
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:43:09.213436Z",
     "start_time": "2025-05-18T08:43:09.176060Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "\n",
    "# 标准化数值型特征\n",
    "scaler = StandardScaler()\n",
    "scaled_num = scaler.fit_transform(all_data[num_cols])\n",
    "\n",
    "# One-Hot编码类别型特征\n",
    "encoder = OneHotEncoder(handle_unknown='ignore')\n",
    "encoded_cat = encoder.fit_transform(all_data[cat_cols]).toarray()\n",
    "\n",
    "# 合并处理后的数据\n",
    "processed_data = pd.concat([pd.DataFrame(scaled_num, columns=num_cols),\n",
    "                            pd.DataFrame(encoded_cat, columns=encoder.get_feature_names_out(cat_cols))],\n",
    "                           axis=1)\n",
    "processed_data"
   ],
   "id": "518a48da2c0ad83e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            Id  MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  \\\n",
       "0    -1.731554    0.067076    -0.189297 -0.214914     0.654724    -0.507514   \n",
       "1    -1.730368   -0.873192     0.534983 -0.067242    -0.058764     2.186285   \n",
       "2    -1.729181    0.067076    -0.044441  0.144637     0.654724    -0.507514   \n",
       "3    -1.727995    0.302142    -0.430724 -0.073662     0.654724    -0.507514   \n",
       "4    -1.726808    0.067076     0.728124  0.531154     1.368211    -0.507514   \n",
       "...        ...         ...          ...       ...          ...          ...   \n",
       "2909  1.725951    2.417743    -2.313852 -1.051385    -1.485738     1.288352   \n",
       "2910  1.727138    2.417743    -2.313852 -1.056778    -1.485738    -0.507514   \n",
       "2911  1.728324   -0.873192     4.397809  1.268235    -0.772251     1.288352   \n",
       "2912  1.729511    0.654742    -0.334153  0.040752    -0.772251    -0.507514   \n",
       "2913  1.730697    0.067076     0.245271 -0.063775     0.654724    -0.507514   \n",
       "\n",
       "      YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  SaleType_ConLw  \\\n",
       "0      1.048131      0.898322    0.545012    0.614748  ...             0.0   \n",
       "1      0.156552     -0.394020   -0.570205    1.236002  ...             0.0   \n",
       "2      0.982088      0.850457    0.351556    0.112263  ...             0.0   \n",
       "3     -1.857756     -0.681207   -0.570205   -0.504423  ...             0.0   \n",
       "4      0.949066      0.754728    1.421254    0.498263  ...             0.0   \n",
       "...         ...           ...         ...         ...  ...             ...   \n",
       "2909  -0.041577     -0.681207   -0.570205   -0.997771  ...             0.0   \n",
       "2910  -0.041577     -0.681207   -0.570205   -0.422198  ...             0.0   \n",
       "2911  -0.371792      0.563270   -0.570205    1.797871  ...             0.0   \n",
       "2912   0.684895      0.371812   -0.570205   -0.228056  ...             0.0   \n",
       "2913   0.717916      0.467541   -0.035356    0.733517  ...             0.0   \n",
       "\n",
       "      SaleType_New  SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  \\\n",
       "0              0.0           0.0          1.0                    0.0   \n",
       "1              0.0           0.0          1.0                    0.0   \n",
       "2              0.0           0.0          1.0                    0.0   \n",
       "3              0.0           0.0          1.0                    1.0   \n",
       "4              0.0           0.0          1.0                    0.0   \n",
       "...            ...           ...          ...                    ...   \n",
       "2909           0.0           0.0          1.0                    0.0   \n",
       "2910           0.0           0.0          1.0                    1.0   \n",
       "2911           0.0           0.0          1.0                    1.0   \n",
       "2912           0.0           0.0          1.0                    0.0   \n",
       "2913           0.0           0.0          1.0                    0.0   \n",
       "\n",
       "      SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  \\\n",
       "0                       0.0                   0.0                   0.0   \n",
       "1                       0.0                   0.0                   0.0   \n",
       "2                       0.0                   0.0                   0.0   \n",
       "3                       0.0                   0.0                   0.0   \n",
       "4                       0.0                   0.0                   0.0   \n",
       "...                     ...                   ...                   ...   \n",
       "2909                    0.0                   0.0                   0.0   \n",
       "2910                    0.0                   0.0                   0.0   \n",
       "2911                    0.0                   0.0                   0.0   \n",
       "2912                    0.0                   0.0                   0.0   \n",
       "2913                    0.0                   0.0                   0.0   \n",
       "\n",
       "      SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                      1.0                    0.0  \n",
       "1                      1.0                    0.0  \n",
       "2                      1.0                    0.0  \n",
       "3                      0.0                    0.0  \n",
       "4                      1.0                    0.0  \n",
       "...                    ...                    ...  \n",
       "2909                   1.0                    0.0  \n",
       "2910                   0.0                    0.0  \n",
       "2911                   0.0                    0.0  \n",
       "2912                   1.0                    0.0  \n",
       "2913                   1.0                    0.0  \n",
       "\n",
       "[2914 rows x 290 columns]"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
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       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
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       "      <td>0.898322</td>\n",
       "      <td>0.545012</td>\n",
       "      <td>0.614748</td>\n",
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       "      <th>1</th>\n",
       "      <td>-1.730368</td>\n",
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       "      <td>0.534983</td>\n",
       "      <td>-0.067242</td>\n",
       "      <td>-0.058764</td>\n",
       "      <td>2.186285</td>\n",
       "      <td>0.156552</td>\n",
       "      <td>-0.394020</td>\n",
       "      <td>-0.570205</td>\n",
       "      <td>1.236002</td>\n",
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       "      <td>0.067076</td>\n",
       "      <td>-0.044441</td>\n",
       "      <td>0.144637</td>\n",
       "      <td>0.654724</td>\n",
       "      <td>-0.507514</td>\n",
       "      <td>0.982088</td>\n",
       "      <td>0.850457</td>\n",
       "      <td>0.351556</td>\n",
       "      <td>0.112263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>-1.727995</td>\n",
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       "      <td>-0.430724</td>\n",
       "      <td>-0.073662</td>\n",
       "      <td>0.654724</td>\n",
       "      <td>-0.507514</td>\n",
       "      <td>-1.857756</td>\n",
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       "      <td>-0.570205</td>\n",
       "      <td>-0.504423</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.726808</td>\n",
       "      <td>0.067076</td>\n",
       "      <td>0.728124</td>\n",
       "      <td>0.531154</td>\n",
       "      <td>1.368211</td>\n",
       "      <td>-0.507514</td>\n",
       "      <td>0.949066</td>\n",
       "      <td>0.754728</td>\n",
       "      <td>1.421254</td>\n",
       "      <td>0.498263</td>\n",
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       "      <td>-1.051385</td>\n",
       "      <td>-1.485738</td>\n",
       "      <td>1.288352</td>\n",
       "      <td>-0.041577</td>\n",
       "      <td>-0.681207</td>\n",
       "      <td>-0.570205</td>\n",
       "      <td>-0.997771</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>1.727138</td>\n",
       "      <td>2.417743</td>\n",
       "      <td>-2.313852</td>\n",
       "      <td>-1.056778</td>\n",
       "      <td>-1.485738</td>\n",
       "      <td>-0.507514</td>\n",
       "      <td>-0.041577</td>\n",
       "      <td>-0.681207</td>\n",
       "      <td>-0.570205</td>\n",
       "      <td>-0.422198</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.288352</td>\n",
       "      <td>-0.371792</td>\n",
       "      <td>0.563270</td>\n",
       "      <td>-0.570205</td>\n",
       "      <td>1.797871</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.729511</td>\n",
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       "      <td>-0.334153</td>\n",
       "      <td>0.040752</td>\n",
       "      <td>-0.772251</td>\n",
       "      <td>-0.507514</td>\n",
       "      <td>0.684895</td>\n",
       "      <td>0.371812</td>\n",
       "      <td>-0.570205</td>\n",
       "      <td>-0.228056</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>1.730697</td>\n",
       "      <td>0.067076</td>\n",
       "      <td>0.245271</td>\n",
       "      <td>-0.063775</td>\n",
       "      <td>0.654724</td>\n",
       "      <td>-0.507514</td>\n",
       "      <td>0.717916</td>\n",
       "      <td>0.467541</td>\n",
       "      <td>-0.035356</td>\n",
       "      <td>0.733517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2914 rows × 290 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 103
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:43:10.773178Z",
     "start_time": "2025-05-18T08:43:10.752006Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 分离处理后训练和测试集\n",
    "all_data = all_data.reset_index(drop=True)\n",
    "processed_data = processed_data.reset_index(drop=True)\n",
    "train_processed = processed_data.loc[all_data['is_train'] == 1].reset_index(drop=True)\n",
    "test_processed = processed_data.loc[all_data['is_train'] == 0].reset_index(drop=True)\n",
    "\n",
    "train = all_data.loc[all_data['is_train'] == 1].reset_index(drop=True)\n",
    "y = train['SalePrice']"
   ],
   "id": "6f2891e5f68a2d1e",
   "outputs": [],
   "execution_count": 104
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:43:12.568425Z",
     "start_time": "2025-05-18T08:43:12.336285Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# PCA降维至3D\n",
    "pca = PCA(n_components=3)\n",
    "pca_data = pca.fit_transform(processed_data)\n",
    "\n",
    "# K-Means肘部法则确定最佳簇数\n",
    "inertia = []\n",
    "for k in range(2, 10):\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(pca_data)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "plt.plot(range(2,10), inertia, marker='o')\n",
    "plt.xlabel('簇数K')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('肘部法则')\n",
    "plt.show()  # 选择K=4:cite[4]:cite[10]\n",
    "\n",
    "# 层次聚类（参数：n_clusters=4，连接方式ward）\n",
    "agg = AgglomerativeClustering(n_clusters=4, linkage='ward')\n",
    "agg_labels = agg.fit_predict(pca_data)\n",
    "agg_labels"
   ],
   "id": "c278622066a1e20a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([2, 3, 2, ..., 3, 0, 2], shape=(2914,))"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 105
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:43:13.573867Z",
     "start_time": "2025-05-18T08:43:13.407259Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 3D聚类散点图（以K-Means为例）\n",
    "kmeans = KMeans(n_clusters=4)\n",
    "clusters = kmeans.fit_predict(pca_data)\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(pca_data[:,0], pca_data[:,1], pca_data[:,2], c=clusters, cmap='viridis', s=20)\n",
    "ax.set_xlabel('PCA1')\n",
    "ax.set_ylabel('PCA2')\n",
    "ax.set_zlabel('PCA3')\n",
    "plt.title('K-Means聚类结果（K=4）')\n",
    "plt.show()\n",
    "\n",
    "# 分析簇特征\n",
    "cluster_profile = all_data.groupby(clusters[:len(all_data)]).agg({\n",
    "    'SalePrice': 'mean',\n",
    "    'GrLivArea': 'median',\n",
    "    'HouseAge': 'median',\n",
    "    'Neighborhood': lambda x: x.mode()[0]\n",
    "})\n",
    "print(cluster_profile)"
   ],
   "id": "14cd77332fa4d034",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       SalePrice  GrLivArea  HouseAge Neighborhood\n",
      "0  177515.174095     1511.0       6.0      Gilbert\n",
      "1  145755.308326     1056.0      48.0        NAmes\n",
      "2  231596.082552     1902.0       5.0      NridgHt\n",
      "3  157450.942105     1639.0      70.0      OldTown\n"
     ]
    }
   ],
   "execution_count": 106
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:43:15.942115Z",
     "start_time": "2025-05-18T08:43:14.620374Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import  mean_squared_error, r2_score, make_scorer\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(train_processed, y, test_size=0.2, random_state=42)\n",
    "\n",
    "models = {\n",
    "    'DecisionTree': DecisionTreeRegressor(random_state=42),   # 用决策树替换线性回归\n",
    "    'RandomForest': RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "}\n",
    "\n",
    "results = {}\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    pred = model.predict(X_val)\n",
    "    results[name] = {\n",
    "        'RMSE': np.sqrt(mean_squared_error(y_val, pred)),\n",
    "        'R²': r2_score(y_val, pred)\n",
    "    }\n",
    "\n",
    "print(pd.DataFrame(results).T)\n",
    "\n",
    "\n",
    "models = {\n",
    "    'DecisionTree': DecisionTreeRegressor(random_state=42),\n",
    "    'RandomForest': RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "}\n",
    "\n",
    "results = {}"
   ],
   "id": "355b0a82d718752b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     RMSE        R²\n",
      "DecisionTree  2204.480428  0.999074\n",
      "RandomForest   966.254241  0.999822\n"
     ]
    }
   ],
   "execution_count": 107
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-18T08:43:29.783926Z",
     "start_time": "2025-05-18T08:43:16.858981Z"
    }
   },
   "cell_type": "code",
   "source": [
    "rmse_scorer = make_scorer(mean_squared_error, squared=False)\n",
    "\n",
    "for name, model in models.items():\n",
    "    # 交叉验证计算RMSE（neg_mean_squared_error先转为正，再开根号）\n",
    "    neg_mse_scores = cross_val_score(model, train_processed, y,\n",
    "                                     scoring='neg_mean_squared_error', cv=5)\n",
    "    rmse_scores = np.sqrt(-neg_mse_scores)\n",
    "    # 交叉验证计算R2\n",
    "    r2_scores = cross_val_score(model, train_processed, y, scoring='r2', cv=5)\n",
    "\n",
    "    results[name] = {\n",
    "        'RMSE Mean': rmse_scores.mean(),\n",
    "        'RMSE Std': rmse_scores.std(),\n",
    "        'R² Mean': r2_scores.mean(),\n",
    "        'R² Std': r2_scores.std()\n",
    "    }\n",
    "\n",
    "print(pd.DataFrame(results).T)"
   ],
   "id": "70651fabb5094492",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                RMSE Mean     RMSE Std   R² Mean    R² Std\n",
      "DecisionTree  4027.449440   829.694621  0.997124  0.001082\n",
      "RandomForest  2679.460846  1448.452035  0.998320  0.001606\n"
     ]
    }
   ],
   "execution_count": 108
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "6238e27e499d021c"
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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